As show in Figure.REF . Firstly, The input images are projected by their intrinsic and extrinsic parameters to Virtual Camera with fixed intrinsic and extrinsic parameters. This process allows for quick harmonization of the intrinsic and extrinsic parameters of the front cameras on different vehicles. Then we use a front view feature extractor to extract the features of front view image. We carried out experiments with ResNet18 and ResNet34 respectively[1]}. In order to better promote the network to extract front view features, front view lane auxiliary supervision is added to the output part of the backbone network. Inspire by [2]}, Then we design a fast multi-scale spatial transformation module based on [3]}, which we call Spatial Transformation Pyramid. This module is responsible for spatial transformation from front view features to BEV features. Finally, we predict the lane on the plane tangent to the local road surface \(P_{road}\)  and \(P_{road}\)  is the plane with \(z=0\)  in the road coordinates \(\mathcal {C}_{road}=\left(x,y,z\right)\) . We divide the \(P_{road}\)  into several cells, one of which represents an area of \(x \times x\) (\(x\)  defaults to 0.5\(m\) ). Inspired by the YOLO[4]} and LaneNet[5]}, we predict the confidence, the embedding used for clustering, the offset from the cell center to the lane in row direction and the average height of each cell. In the inference, We use a fast clustering method to fuse the results of each branch into lanes.
